Predicting Traffic Flow Propagation Based on Congestion at Neighbouring Roads Using Hidden Markov Model

Priambodo, Bagus; Ahmad, Azlina; Kadir, Rabiah Abdul

doi:10.1109/access.2021.3075911

Cited by 12 publications

(2 citation statements)

References 48 publications

(50 reference statements)

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“…In another study, the authors overcome the limitations of adaptive K-means clustering and use the pattern by using regression models. In this study, the Markov model was employed to identify traffic patterns to predict the congestion [ 51 , 52 ]. We proposed the Markov approach because it does not require a lot of data, and it can predict the future state by using only the data from the current state.…”

Section: Proposed Modelmentioning

confidence: 99%

A Novel Markov Model-Based Traffic Density Estimation Technique for Intelligent Transportation System

Beenish

Javid

Fahad

et al. 2023

Sensors

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An intelligent transportation system (ITS) aims to improve traffic efficiency by integrating innovative sensing, control, and communications technologies. The industrial Internet of things (IIoT) and Industrial Revolution 4.0 recently merged to design the industrial Internet of things–intelligent transportation system (IIoT-ITS). IIoT sensing technologies play a significant role in acquiring raw data. The application continuously performs the complex task of managing traffic flows effectively based on several parameters, including the number of vehicles in the system, their location, and time. Traffic density estimation (TDE) is another important derived parameter desirable to keep track of the dynamic state of traffic volume. The expanding number of vehicles based on wireless connectivity provides new potential to predict traffic density more accurately and in real time as previously used methodologies. We explore the topic of assessing traffic density by using only a few simple metrics, such as the number of surrounding vehicles and disseminating beacons to roadside units and vice versa. This research paper investigates TDE techniques and presents a novel Markov model-based TDE technique for ITS. Finally, an OMNET++-based approach with an implementation of a significant modification of a traffic model combined with mathematical modeling of the Markov model is presented. It is intended for the study of real-world traffic traces, the identification of model parameters, and the development of simulated traffic.

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Section: Proposed Modelmentioning

confidence: 99%

A Novel Markov Model-Based Traffic Density Estimation Technique for Intelligent Transportation System

Beenish

Javid

Fahad

et al. 2023

Sensors

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show abstract

“…This graph is used to calculate a similarity matrix that enables the clustering of the case notions based on a threshold. Markov clustering is selected as it is widely applied in practice for different purposes, e.g., identifying protein-protein interaction networks [14], traffic state clustering [16], document clustering [11], and comparing similarities between different process variants [12]. To answer the second research question, a threshold tuning algorithm is defined to identify sets of different clusters that can be discovered based on all possible thresholds.…”

Section: Introductionmentioning

confidence: 99%

Object Type Clustering Using Markov Directly-Follow Multigraph in Object-Centric Process Mining

Jalali

2022

IEEE Access

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Object-centric process mining is a new process mining paradigm with more realistic assumptions about underlying data by considering several case notions, e.g., an order handling process can be analyzed based on order, item, package, and route case notions. Including many case notions can result in a very complex model. To cope with such complexity, this paper introduces a new approach to cluster similar case notions based on Markov Directly-Follow Multigraph, which is an extended version of the wellknown Directly-Follow Graph supported by many industrial and academic process mining tools. This graph is used to calculate a similarity matrix for discovering clusters of similar case notions based on a threshold. A threshold tuning algorithm is also defined to identify sets of different clusters that can be discovered based on different levels of similarity. Thus, the cluster discovery will not rely merely on analysts' assumptions. The approach is implemented and released as a part of a python library, called processmining, and it is evaluated through a Purchase-to-Pay (P2P) object-centric event log file. The discovered clusters are evaluated by discovering Directly Follow-Multigraph by flattening the log based on the clusters. The similarity between identified clusters is also evaluated by calculating the similarity between the behavior of the process models discovered for each case notion using inductive miner based on footprints conformance checking.INDEX TERMS object-centric process mining, object-centric directly-follow multigraph, markov clustering VOLUME 4, 2016

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Predict Traffic State Based on PCA-KMeans Clustering of neighbouring roads

Priambodo,

Jokonowo,

Samidi

et al. 2023

Advances in Visual Informatics

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Predicting Traffic Flow Propagation Based on Congestion at Neighbouring Roads Using Hidden Markov Model

Cited by 12 publications

References 48 publications

A Novel Markov Model-Based Traffic Density Estimation Technique for Intelligent Transportation System

A Novel Markov Model-Based Traffic Density Estimation Technique for Intelligent Transportation System

Object Type Clustering Using Markov Directly-Follow Multigraph in Object-Centric Process Mining

Predict Traffic State Based on PCA-KMeans Clustering of neighbouring roads

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